code: def evaluate(
      split: dataset.Split,
      params: hk.Params,
      state: hk.State,
  ) -> Scalars:
      """Evaluates the model at the given params/state."""
      if split.num_examples % FLAGS.eval_batch_size:
          raise ValueError(
              f"Eval batch size {FLAGS.eval_batch_size} must be a "
              f"multiple of {split} num examples {split.num_examples}"
          )
  
      # Params/state are sharded per-device during training. We just need the copy
      # from the first device (since we do not pmap evaluation at the moment).
      params, state = jax.tree_util.tree_map(
          lambda x: x[0], (params, state)
      )
      test_dataset = dataset.load(
          split,
          is_training=False,
          batch_dims=[FLAGS.eval_batch_size],
          transpose=FLAGS.dataset_transpose,
          zeros=FLAGS.dataset_zeros,
      )
      correct = jnp.array(0)
      total = 0
      for batch in test_dataset:
          correct += eval_batch(params, state, batch)
          total += batch["labels"].shape[0]
      assert total == split.num_examples, total
      return {"top_1_acc": correct.item() / total}
  
imports: from collections.abc import Iterable, Mapping
  import contextlib
  import functools
  import timeit
  from typing import NamedTuple
  from absl import app
  from absl import flags
  from absl import logging
  import haiku
  from examples.imagenet import dataset
  import jax
  import jax.numpy
  import jmp
  import numpy
  import optax
  import tree
label_desc: Use image node_id 10 for calling a global function with example usage: evaluate(split, params, state) and returns: dict